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A study of drift analysis for estimating computation time of evolutionary algorithms

โœ Scribed by Jun He; Xin Yao

Book ID
111601422
Publisher
Springer Netherlands
Year
2004
Tongue
English
Weight
103 KB
Volume
3
Category
Article
ISSN
1567-7818

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๐Ÿ“œ SIMILAR VOLUMES

Drift analysis and average time complexi
Drift analysis and average time complexity of evolutionary algorithms
โœ Jun He; Xin Yao ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 212 KB

The computational time complexity is an important topic in the theory of evolutionary algorithms (EAs). This paper reports some new results on the average time complexity of EAs. Based on drift analysis, some useful drift conditions for deriving the time complexity of EAs are studied, including cond

Towards an analytic framework for analys
Towards an analytic framework for analysing the computation time of evolutionary algorithms
โœ Jun He; Xin Yao ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 296 KB

In spite of many applications of evolutionary algorithms in optimisation, theoretical results on the computation time and time complexity of evolutionary algorithms on different optimisation problems are relatively few. It is still unclear when an evolutionary algorithm is expected to solve an optim

Erratum to: Drift analysis and average t
Erratum to: Drift analysis and average time complexity of evolutionary algorithms: [Artificial Intelligence 127 (2001) 57โ€“85]
โœ Jun He; Xin Yao ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 54 KB

The proof of Theorem 6 in the paper by J. He and X. Yao [Artificial Intelligence 127 (1) (2001) 57-85] contains a mistake, although the theorem is correct [S. Droste et al., Theoret. Comput. Sci. 276 (2002) 51-81]. This note gives a revised proof and theorem. It turns out that the revised theorem is